Feed types driven differentiation of microbial community and functionality in marine integrated multitrophic aquaculture system

Integrated multi trophic aquaculture (IMTA) improves the production of aquatic animals by promoting nutrient utilization through different tropical levels. Microorganisms play an important role in elements cycling, energy flow and farmed-species health. The aim of this study was to evaluate how feed types, fresh frozen fish diet (FFD) or formulated diet (FD), influence the microbial community diversity and functionality in both water and sediment in a marine IMTA system. Preferable water quality, higher animal yields and higher cost efficiency were achieved in the FD pond. Feed types changed the pond bacterial community distribution, especially in the rearing water. The FFD pond was dominated with Cyanobacteria in the water, which played an important role in nitrogen fixation through photosynthesis due to the high nitrogen input of the frozen fish diet. The high carbohydrate composition in the formulated diet triggered higher metabolic pathways related to carbon and lipid metabolism in the water of the FD pond. Sediment had significantly higher microbial diversity than the rearing water. In sediment, the dominating genus, Sulfurovum and Desulfobulbus, were found to be positively correlated by network analysis, which had similar functionality in sulfur transformation. The relatively higher rates of antibiotic biosynthesis in the FFD sediment might be related to the pathogenic bacteria introduced by the trash fish diet. The difference in microbial community composition and metabolic pathways may be associated with the different pathways for nutrient cycling and animal growth performance. The formulated diet was determined to be more ecologically and economically sustainable than the frozen fish diet for marine IMTA pond systems.</p

Fatigue Properties of Rubber Modified SMA Asphalt Mixture

To evaluate fatigue propertiesof rubber modified SMA asphaltmixture, based on optimizing the preparation process of rubber asphalt, the splitting fatigue testsof three kinds of asphalt mixture is carried out including ordinary asphalt, modified asphalt with rubber powder and SBS modified asphalt. And the freezing and thawing conditions were considered in the tests. The results show that rubber powder can improve the fatigue properties of SMA asphalt mixture, but the modified effect of rubber powder is not as good as SBS modifier, the fatigue properties of asphalt mixture are deteriorated by freeze-thaw conditions, the deterioration degree of rubber modified asphalt mixture is less than SBS modified asphalt mixture, but rubber modified asphalt mixture has better freeze-thaw durability

Winding Clearness for Differentiable Point Cloud Optimization

We propose to explore the properties of raw point clouds through the \emph{winding clearness}, a concept we first introduce for assessing the clarity of the interior/exterior relationships represented by the winding number field of the point cloud. In geometric modeling, the winding number is a powerful tool for distinguishing the interior and exterior of a given surface $\partial \Omega$, and it has been previously used for point normal orientation and surface reconstruction. In this work, we introduce a novel approach to assess and optimize the quality of point clouds based on the winding clearness. We observe that point clouds with reduced noise tend to exhibit improved winding clearness. Accordingly, we propose an objective function that quantifies the error in winding clearness, solely utilizing the positions of the point clouds. Moreover, we demonstrate that the winding clearness error is differentiable and can serve as a loss function in optimization-based and learning-based point cloud processing. In the optimization-based method, the loss function is directly back-propagated to update the point positions, resulting in an overall improvement of the point cloud. In the learning-based method, we incorporate the winding clearness as a geometric constraint in the diffusion-based 3D generative model. Experimental results demonstrate the effectiveness of optimizing the winding clearness in enhancing the quality of the point clouds. Our method exhibits superior performance in handling noisy point clouds with thin structures, highlighting the benefits of the global perspective enabled by the winding number

Weak Supervision for Fake News Detection via Reinforcement Learning

Today social media has become the primary source for news. Via social media platforms, fake news travel at unprecedented speeds, reach global audiences and put users and communities at great risk. Therefore, it is extremely important to detect fake news as early as possible. Recently, deep learning based approaches have shown improved performance in fake news detection. However, the training of such models requires a large amount of labeled data, but manual annotation is time-consuming and expensive. Moreover, due to the dynamic nature of news, annotated samples may become outdated quickly and cannot represent the news articles on newly emerged events. Therefore, how to obtain fresh and high-quality labeled samples is the major challenge in employing deep learning models for fake news detection. In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection. The proposed framework consists of three main components: the annotator, the reinforced selector and the fake news detector. The annotator can automatically assign weak labels for unlabeled news based on users' reports. The reinforced selector using reinforcement learning techniques chooses high-quality samples from the weakly labeled data and filters out those low-quality ones that may degrade the detector's prediction performance. The fake news detector aims to identify fake news based on the news content. We tested the proposed framework on a large collection of news articles published via WeChat official accounts and associated user reports. Extensive experiments on this dataset show that the proposed WeFEND model achieves the best performance compared with the state-of-the-art methods.Comment: AAAI 202

Prognostic biomarker DARS2 correlated with immune infiltrates in bladder tumor

BackgroundDARS2 is a pivotal member of the Aminoacyl-tRNA synthetases family that is critical for regulating protein translation. However, the biological role of DARS2 in bladder cancer remains elusive.MethodsWe analyzed the correlation between DARS2 expression and prognosis, tumor stage, and immune infiltration in bladder cancer using The Cancer Genome Atlas (TCGA) database. We validated findings in clinical samples from The First Affiliated Hospital of Nanchang University and explored the biological functions of DARS2 using cell and animal models.ResultsWe found DARS2 to be upregulated in bladder cancer, associated with tumor progression and poor prognosis. Immune infiltration analysis suggested that DARS2 may facilitate immune evasion by modulating PD-L1. Cell and animal experiments validated that DARS2 knockdown and overexpress can inhibit or increase cancer cell proliferation, metastasis, tumorigenesis, immune escape, and PD-L1 levels.ConclusionsOur study reveals DARS2 as a potential prognostic biomarker and immunotherapy target in BLCA

Cross-Correlation Forecast of CSST Spectroscopic Galaxy and MeerKAT Neutral Hydrogen Intensity Mapping Surveys

Cross-correlating the data of neutral hydrogen (HI) 21cm intensity mapping with galaxy surveys is an effective method to extract astrophysical and cosmological information. In this work, we investigate the cross-correlation of MeerKAT single-dish mode HI intensity mapping and China Space Station Telescope (CSST) spectroscopic galaxy surveys. We simulate a survey area of $\sim 300$ $\mathrm{deg}^2$ of MeerKAT and CSST surveys at $z=0.5$ using Multi-Dark N-body simulation. The PCA algorithm is applied to remove the foregrounds of HI intensity mapping, and signal compensation is considered to solve the signal loss problem in the HI-galaxy cross power spectrum caused by the foreground removal process. We find that from CSST galaxy auto and MeerKAT-CSST cross power spectra, the constraint accuracy of the parameter product $\Omega_{\rm HI}b_{\rm HI}r_{{\rm HI},g}$ can reach to $\sim1\%$, which is about one order of magnitude higher than the current results. After performing the full MeerKAT HI intensity mapping survey with 5000 deg$^2$ survey area, the accuracy can be enhanced to $<0.3\%$. This implies that the MeerKAT-CSST cross-correlation can be a powerful tool to probe the cosmic HI property and the evolution of galaxies and the Universe.Comment: 17 pages, 11 figures, 3 tables. Accepted for publication in RA